Jon A. Wellner

Weak Convergence and Empirical Processes

1.1. Introduction.- 1.2. Outer Integrals and Measurable Majorants.- 1.3. Weak Convergence.- 1.4. Product Spaces.- 1.5. Spaces of Bounded Functions.- 1.6. Spaces of Locally Bounded Functions.- 1.7. The Ball Sigma-Field and Measurability of Suprema.- 1.8. Hilbert Spaces.- 1.9. Convergence: Almost Surely and in Probability.- 1.10. Convergence: Weak, Almost Uniform, and in Probability.- 1.11. Refinements.- 1.12. Uniformity and Metrization.- 2.1. Introduction.- 2.2. Maximal Inequalities and Covering Numbers.- 2.3. Symmetrization and Measurability.- 2.4. Glivenko-Cantelli Theorems.- 2.5. Donsker Theorems.- 2.6. Uniform Entropy Numbers.- 2.7. Bracketing Numbers.- 2.8. Uniformity in the Underlying Distribution.- 2.9. Multiplier Central Limit Theorems.- 2.10. Permanence of the Donsker Property.- 2.11. The Central Limit Theorem for Processes.- 2.12. Partial-Sum Processes.- 2.13. Other Donsker Classes.- 2.14. Tail Bounds.- 3.1. Introduction.- 3.2. M-Estimators.- 3.3. Z-Estimators.- 3.4. Rates of Convergence.- 3.5. Random Sample Size, Poissonization and Kac Processes.- 3.6. The Bootstrap.- 3.7. The Two-Sample Problem.- 3.8. Independence Empirical Processes.- 3.9. The Delta-Method.- 3.10. Contiguity.- 3.11. Convolution and Minimax Theorems.- A. Appendix.- A.1. Inequalities.- A.2. Gaussian Processes.- A.2.1. Inequalities and Gaussian Comparison.- A.2.2. Exponential Bounds.- A.2.3. Majorizing Measures.- A.2.4. Further Results.- A.3. Rademacher Processes.- A.4. Isoperimetric Inequalities for Product Measures.- A.5. Some Limit Theorems.- A.6. More Inequalities.- A.6.1. Binomial Random Variables.- A.6.2. Multinomial Random Vectors.- A.6.3. Rademacher Sums.- Notes.- References.- Author Index.- List of Symbols.

Efficient and adaptive estimation for semiparametric models

Introduction.- Asymptotic Inference for (Finite-Dimensional) Parametric Models.- Information Bounds for Euclidean Parameters in Infinite-Dimensional Models.- Euclidean Parameters: Further Examples.- Information Bounds for Infinite-Dimensional Parameters.- Infinite-Dimensional Parameters: Further Examples: Construction of Examples.

Information bounds and nonparametric maximum likelihood estimation

I. Information Bounds.- 1 Models, scores, and tangent spaces.- 1.1 Introduction.- 1.2 Models P.- 1.3 Scores: Differentiability of the Model.- 1.4 Tangent Sets P0 and Tangent Spaces P.- 1.5 Score Operators.- 1.6 Exercises.- 2 Convolution and asymptotic minimax theorems.- 2.1 Introduction.- 2.2 Finite-dimensional Parameter Spaces.- 2.3 Infinite-dimensional Parameter Spaces.- 2.4 Exercises.- 3 Van der Vaarts Differentiability Theorem.- 3.1 Differentiability of Implicitly Defined Functions.- 3.2 Some Applications of the Differentiability Theorem.- 3.3 Exercises.- II. Nonparametric Maximum Likelihood Estimation.- 1 The interval censoring problem.- 1.1 Characterization of the non-parametric maximum likelihood estimators.- 1.2Exercises.- 2 The deconvolution problem.- 2.1 Decreasing densities and non-negative random variables.- 2.2 Convolution with symmetric densities.- 2.3 Exercises.- 3 Algorithms.- 3.1 The EM algorithm.- 3.2 The iterative convex minorant algorithm.- 3.3 Exercises.- 4 Consistency.- 4.1 Interval censoring, Case 1.- 4.2 Convolution with a symmetric density.- 4.3 Interval censoring, Case 2.- 4.4 Exercises.- 5 Distribution theory.- 5.1 Interval censoring, Case 1.- 5.2 Interval censoring, Case 2.- 5.3 Deconvolution with a decreasing density.- 5.4 Estimation of the mean.- 5.5 Exercises.- References.

A Hybrid Algorithm for Computation of the Nonparametric Maximum Likelihood Estimator from Censored Data

Abstract We present a hybrid algorithm for nonparametric maximum likelihood estimation from censored data when the log-likelihood is concave. The hybrid algorithm uses a composite algorithmic mapping combining the expectation-maximization (EM) algorithm and the (modified) iterative convex minorant (ICM) algorithm. Global convergence of the hybrid algorithm is proven; the iterates generated by the hybrid algorithm are shown to converge to the nonparametric maximum likelihood estimator (NPMLE) unambiguously. Numerical simulations demonstrate that the hybrid algorithm converges more rapidly than either of the EM or the naive ICM algorithm for doubly censored data. The speed of the hybrid algorithm makes it possible to accompany the NPMLE with bootstrap confidence bands.

Interval Censored Survival Data: A Review of Recent Progress

We review estimation in interval censoring models, including nonparametric estimation of a distribution function and estimation of regression models. In the nonparametric setting, we describe computational procedures and asymptotic properties of the nonparametric maximum likelihood estimators. In the regression setting, we focus on the proportional hazards, the proportional odds and the accelerated failure time semiparametric regression models. Particular emphasis is given to calculation of the Fisher information for the regression parameters. We also discuss computation of the regression parameter estimators via profile likelihood or maximization of the semiparametric likelihood, distributional results for the maximum likelihood estimators, and estimation of (asymptotic) variances. Some further problems and open questions are also reviewed.

High dimensional probability III

I. Measures on General Spaces and Inequalities.- Stochastic inequalities and perfect independence.- Prokhorov-LeCam-Varadarajans compactness criteria for vector measures on metric spaces.- On measures in locally convex spaces.- II. Gaussian Processes.- Karhunen-Loeve expansions for weighted Wiener processes and Brownian bridges via Bessel functions.- Extension du theoreme de Cameron-Martin aux translations aleatoires. II. Integrabilite des densites.- III. Limit Theorems.- Rates of convergence for Levys modulus of continuity and Hinchins law of the iterated logarithm.- On the limit set in the law of the iterated logarithm for U-statistics of order two.- Perturbation approach applied to the asymptotic study of random operators.- A uniform functional law of the logarithm for a local Gaussian process.- Strong limit theorems for mixing random variables with values in Hilbert space and their applications.- IV. Local Times.- Local time-space calculus and extensions of Itos formula.- Local times on curves and surfaces.- V. Large, Small Deviations.- Large deviations of empirical processes.- Small deviation estimates for some additive processes.- VI. Density Estimation.- Convergence in distribution of self-normalized sup-norms of kernel density estimators.- Estimates of the rate of approximation in the CLT for L1-norm of density estimators.- VII. Statistics via Empirical Process Theory.- Statistical nearly universal Glivenko-Cantelli classes.- Smoothed empirical processes and the bootstrap.- A note on the asymptotic distribution of Berk-Jones type statistics under the null hypothesis.- A note on the smoothed bootstrap.

Computing Chernoff's Distribution

A distribution that arises in problems of estimation of monotone functions is that of the location of the maximum of two-sided Brownian motion minus a parabola. Using results from the first authors earlier work, we present algorithms and programs for computation of this distribution and its quantiles. We also present some comparisons with earlier computations and simulations.

Preservation Theorems for Glivenko-Cantelli and Uniform Glivenko-Cantelli Classes

We show that the P—Glivenko property of classes of functions F 1,…,F k is preserved by a continuous function ϕ from R k to R in the sense that the new class of functions

Strong limit theorems for oscillation moduli of the uniform empirical process

x \to \varphi ({f_1}(x), \ldots ,{f_k}(x)),{f_i} \in {F_i},i = 1, \ldots ,k